a visualisation tool for Transcriptional Regulatory Network Differential analysis

The advent of next generation sequencing technologies has seen explosive growth in genomic data, and dense coverage of related organisms, supporting study of subtle, strain-specific variations as a determinant of function. The challenge thus lies in comparing models of complex relationships, were once restricted to a single genome, across hundreds of sequences. Transcriptional Regulatory Network (TRN) structures document the influence of regulatory proteins called Transcription Factors (TFs) on associated Target Genes (TGs). TRNs are routinely inferred from model systems or iterative search, and analysis requires simultaneous displays of multiple networks well beyond those of existing network visualisation tools (Gehlenborg et al., 2010). TRNDiff supports the comparative analysis and visualization of TRNs (and similarly structured data) from many genomes, allowing rapid identification of functional variations across strains.

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Reconstructing TRNs based on the Regulog approach

A widely used assumption to infer regulatory interactions follows: given a known interaction between a TF and TG in a model system, if both the orthologous TF and TG are found in a target genome, then it is assumed the orthologous TF regulates the orthologous TG (citation). This is referred to as the regulog assumption. Traditional regulatory network structures based on this approach display an edge to represent the regulatory relationship. However, this approach does not take into account the identification of transcription factor binding sites (TFBSs), which is becoming the standard approach. We include TFBSs in our construction when visualising such regulatory networks.